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Comparative genomics: Overview & Tools
Urmila Kulkarni-Kale
Bioinformatics Centre
University of Pune, Pune 411 007.
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genome sequence: Fact file
• 1995: The first complete genome sequence of Haemophilus infuenzae Rd-was published
• Biological systems are dynamic and evolving• The forth dimension: Time• Genome sequence is a snapshot of evolution• Correlation between Phenotypic properties and
Genomic region is not straightforward as phenotypic properties are result of many to many interactions
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genomes: the current status
• Published complete genomes: 303
– Archaeal: 24
– Bacterial: 240
– Eukaryal: 39
• Completed Viral genomes: >5000
• Prokaryotic ongoing genomes: 755
• Eukaryotic ongoing genomes: 531
As of October 11, 2005
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genome databases• Genomes at NCBI, EBI, TIGR
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Function information clock of E. coli
Generated on March 2K4
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genome analyses
• Variation in – Genome size– GC content – Codon usage– Amino acid composition– Genome organisation
• Single circular chromosomes
• Linear chromosome + extra chromosomal elements
G, A, P, R: GC richI, F, Y, M, D: AT rich
E. coli: 4.6MbpM. pneumoniae: 0.81Mbp
B. subtilis: 4.20Mbp
B. burgdorferi: 29%M. tuberculosis: 68%
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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CG: Comparisons between genomes
• The stains of the same species
• The closely related species
• The distantly related species– List of Orthologs – Evolution of individual genes – Evolution of organisms
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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CG helps to ask some interesting questions
• Identification similarities/differences between genomes may allow us to understand :– How 2 organisms evolved?– Why certain bacteria cause diseases while
others do not?– Identification and prioritization of drug targets
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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CG: Unit of comparison• Unit of comparison: Gene/Genome
– Number– Content (sequence)– Location (map position)– Gene Order– Gene Cluster (Genes that are part of a known metabolic
pathway, are found to exist as a group)– Colinearity of gene order is referred as synteny– A conserved group of genes in the same order in two
genomes as a syntenic groups or syntenic clusters– Translocation: movement of genomic part from one
position to another
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Comparison of the coding regions
• Begins with the gene identification algorithm: infer what portions of the genomic sequence actively code for genes.
• There are four basic approaches.
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Knowledge of Full Genome sequence: Solutions or new questions…?
• Still struggling with the gene counters…
Correct # of
genes…?
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Structure of tryptophan operon • Numbers: Gene number• Arrows: Direction of transcription• //: Dispersion of operon by 50 genes
Domain fusiontrpD and trpGtrpF and trpC
trpB and trpAgenetically linked
separate genes
Dan
deka
r et
al.,
199
8
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Important observations with regard to Gene Order
• Order is highly conserved in closely related species but gets changed by rearrangements
• With more evolutionary distance, no correspondence between the gene order of orthologous genes
• Group of genes having similar biochemical function tend to remain localized– Genes required for synthesis of tryptophan (trp
genes) in E. coli and other prokaryotes
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Synteny
• Refers to regions of two genomes that show considerable similarity in terms of – sequence and – conservation of the order of genes
• likely to be related by common descent.
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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COGs: Phylogenetic classification of proteins
encoded in complete genomes
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genome analyses@NCBIPairwise genome comparison of protein
homologs (symmetrical best hits)
http://www.ncbi.nlm.nih.gov/sutils/geneplot.cgi
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Integr8: CG site at EBIhttp://www.ebi.ac.uk/integr8
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Comparative Genomics Tools
• BLAST2 • MUMmer• Comparisons and analyses at both
– Nucleic acid and protein level
• Comparative genomics of Parasites @ TIGR• Microbial Genome Database (MDG) in Japan• Comparative Genome analysis in P. Borks lab
@embl-heidelberg• Comprehensive Microbial Resource page@TIGR
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genome Alignment Algorithm:MUMmer
• Developed by – Dr. Steven Salzberg’s group at TIGR– NAR (1999) 27:2369-2376– NAR (2002) 30:2478-2483
• Availability– Free– TIGR site
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Features of MUMmer• The algorithm assumes that sequences are closely
related• Can quickly compare millions of bases• Outputs:
– Base to base alignment– Highlights the exact matches and differences in the
genomes– Locates
• SNPs• Large inserts• Significant repeats• Tandem repeats and reversals
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Definitions are drawn from biology• SNP: Single mutation surrounded by two
matching regions– Regions of DNA where 2 sequences have diverged by
more than one SNP
• Large inserts: regions inserted into one of the genomes – Sequence reversals, lateral gene transfer
• Repeats: the form of duplication that has occurred in either genome.
• Tandem repeats: regions of repeated DNA in immediate succession but with different copy number in different genomes.– A repeat can occur 2.5 times
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Techniques used in the MUMmer Algorithm
Compute Suffix trees for every genome
Longest Increasing Subsequence (LIS)
Alignment using Smith & Waterman algorithm
Integration ofthese techniques
for genome alignment
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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MUMmer: Steps in the alignment process
Read two genomes
Perform Maximum Unique Match (MUM) of genomes
Sort and order the MUMs using LIS
Close the gaps in the
Alignment
Using SNPs, mutation regions, repeats, tandem
repeats
Output alignment
• MUMs• regions that do not match exactly
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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MUMmer steps
• Locating MUMs
• Sorting MUMs
• Closure with gaps
G1: ACTGATTACGTGAACTGGATCCA
G2: ACTCTAGGTGAAGTGATCCA
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Genome1: ACTGATTACGTGAACTGGATCCAGenome2: ACTCTAGGTGAAGTGATCCA
Genome1: ACTGATTACGTGAACTGGATCCA
Genome2: ACTCTAGGTGAAGTGATCCA
ACTGATTACGTGAACTGGATCCA
ACTC--TAGGTGAAGT-GATCCA
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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What is a MUM?• MUM is a subsequence that occurs exactly once in
both genomes and is NOT part of any longer sequence
• Two characters that bound a MUM are always mismatches
• Principle: if a long matching sequence occurs exactly once in each genome, it is certainly to be part of global alignment
GenA: tcgatcGACGATCGCCGCCGTAGATCGAATAACGAGAGAGCATAAcgacttaGenB: gcattaGACGATCGCCGCCGTAGATCGAATAACGAGAGAGCATAAtccagag
Similar to BLAST & FASTA!!
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Sorting & ordering MUMs• MUMs are sorted according to their position in
Genome A• The order of matching MUMs in Genome B is
considered
• LIS algorithm to locate longest set of MUMs which occur in ascending order in both genomes
2 4
MUM5:transposition
MUM3:Random matchInexact repeat
Leads to Global MUM-alignment
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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MUMmer Results
• 2 strains of M. tuberculosis– H37Rv & CDC1551 – Genome size: 4Mb– Time: 55 s
• Generating suffix tree: 5 s
• Sorting MUMs: 45s
• S&W alignment: 5 s
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Alignment of M. tuberculosis strainsCDC1551 (Top) & H37Rv (bottom)
Single green lines indicate SNPs
Blue lines indicate insertions
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Comparison of 2 Mycoplasma genomescousins that are distantly related
• M. genitalium: 580 074 nt• M. pneumoniae: 816 394 (+226 000)• Analysis of proteins tell us that all M.g.
proteins are present in P.m. • Alignment was carried using
– FASTA (dividing each genome into 1000 bp)– All-against-all searches– Fixed length of pattern (25)– Using MUMmer (length = 25)
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Comparison of 2 Mycoplasma genomes
Using FASTA
Fixed length patterns: 25mers
MUMmer
October 2K5 © UKK, Bioinformatics Centre, University of Pune.
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Post-sequencing challenges • Genome sequencing is just the beginning to
appreciate biocomplexity • Sequence-based function assignment approaches
fail as the sequence similarity drops …• Structure-based function prediction approaches are
limited by the availability of structures, association of structural motifs & associated functional descriptor
• As a result, in any genome,
Genes with unknown function: ~60%
Genes with known function: ~ 40%